Update app.py

app.py

@@ -1,29 +1,26 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("bert-base-uncased")  # Replace with a relevant model for classification or detection
+# Initialize the Hugging Face Inference client with a more relevant model (e.g., a fine-tuned password detection model)
+client = InferenceClient("username/password-detection-model")  # Replace with your trained model
 
 def detect_passwords(text, threshold=0.9):
     """
-    Simulates BERT model usage to detect possible passwords.
+    Detects potential passwords in text using a model from Hugging Face.
     :param text: Input text containing potential passwords.
     :param threshold: Confidence score above which a pattern is flagged.
     :return: Flagged patterns and their confidence scores.
     """
-    # Simulated model output (replace with actual BERT inference if supported)
-    tokens = text.split()  # Tokenize the input text (basic example)
-    flagged_items = []
-    for token in tokens:
-        # Simulate a condition where tokens matching a simple regex-like pattern are flagged
-        if len(token) > 6 and any(c.isdigit() for c in token) and any(c.isalpha() for c in token):
-            # Simulate confidence score
-            confidence_score = 0.95  # Example confidence for demo purposes
-            if confidence_score > threshold:
-                flagged_items.append((token, confidence_score))
+    # Using a model inference to classify potential passwords
+    response = client.query({"inputs": text})  # Query the model for classification
+    predictions = response.get("predictions", [])
     
+    flagged_items = []
+    for pred in predictions:
+        token, confidence_score = pred["token"], pred["score"]
+        if confidence_score > threshold:
+            flagged_items.append((token, confidence_score))
+
     if not flagged_items:
         return "No passwords detected."
     else:
@@ -49,16 +46,14 @@ def respond(
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
-    # Simulate BERT-driven password detection for the user's message
+    # Use Hugging Face model to detect passwords in the user's message
     detected_passwords = detect_passwords(message)
     response = detected_passwords
 
-    return response  # Outputs directly for demo purposes
+    return response  # Output the result
 
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Gradio Interface for interaction
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -75,6 +70,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()